A well-designed communication network that provides reliable quality of service (QoS) guarantees is a critical platform on which a variety of novel cyber-physical system (CPS) applications can be built. When designed well, the CPS entities can seamlessly talk to each other, and the network acts merely as a transparent enabler. When designed poorly, bottlenecks, congestions, delays, etc. that develop can choke not only CPS applications and but also the development of new CPS applications.

We are at a point in time where large scale IoT deployments, those that can improve delivery of societal services, are just beginning – smart meters at homes, safety monitoring of equipments, mobile health, improved distribution and leakage detection in water networks, among many others. Many innovative services are also being developed around such applications. It will not be long before IoT data starts flooding the network. It is therefore crucial that the right design decision choices are made at this stage, when the IoT networks are being planned, because once deployed, there is always reluctance to modify them.

IoT networks provide a significant challenge because end-nodes have limited power resources, for example, they may operate on batteries that are either never replaced or are charged via harvested energies bringing stochasticity into the picture. Such devices may also have to communicate over large distances in some (perhaps rural) regions, but may carry only sporadic (but usually light) traffic, and this sparsity must be intelligently exploited. Additionally, good deployments require tedious measurements by skilled engineers. The goal of this project is to identify IoT network design methodologies that can deal with the resource challenges highlighted above.

Design more robust physical layer technologies via coding that exploits polarisation diversity. This will allow for random deployments by subsequently enabling the network to self-heal and improve diversity order in case of random realisations that involve deep shadows.

Provide a model for deployment outcomes and for traffic, and identify system-optimal operating points. This will improve the QoS and overall throughput.

It is anticipated that this project will feed into the Smart Cities project’s goals.

Project Publications

1.

Rathod, Nihesh; Subramanian, Renu; Sundaresan, Rajesh

A GIS- and measurement-aided framework for placement of relays in a heterogeneous propagation environment Presentation

@conference{Krishnaswamy2017,
title = {Augmenting max-weight with explicit learning for wireless scheduling with switching costs},
author = {S. Krishnaswamy and P. T. Akhil and A. Arapostathis and S. Shakkottai and Rajesh Sundaresan},
url = {http://www.rbccps.org/wp-content/uploads/2018/01/08056983.pdf},
doi = {10.1109/INFOCOM.2017.8056983},
year = {2017},
date = {2017-10-05},
booktitle = {Proceedings of the 2017 IEEE International Conference on Computer Communications (INFOCOM), 01.-04.05.17, Atlanta (USA)},
abstract = {In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to opportunistically switch off a subset of BSs to minimize energy costs. We consider two types of energy cost: (i) the cost of maintaining a BS in the active state, and (ii) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach – a Max-Weight algorithm along with a Lyapunov-based stability argument – does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight based channel scheduler that has fast temporal dynamics. We show using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to opportunistically switch off a subset of BSs to minimize energy costs. We consider two types of energy cost: (i) the cost of maintaining a BS in the active state, and (ii) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach – a Max-Weight algorithm along with a Lyapunov-based stability argument – does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight based channel scheduler that has fast temporal dynamics. We show using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability.